Detecting sensor degradation by actively controlling an autonomous vehicle

ABSTRACT

Methods and systems are disclosed for determining sensor degradation by actively controlling an autonomous vehicle. Determining sensor degradation may include obtaining sensor readings from a sensor of an autonomous vehicle, and determining baseline state information from the obtained sensor readings. A movement characteristic of the autonomous vehicle, such as speed or position, may then be changed. The sensor may then obtain additional sensor readings, and second state information may be determined from these additional sensor readings. Expected state information may be determined from the baseline state information and the change in the movement characteristic of the autonomous vehicle. A comparison of the expected state information and the second state information may then be performed. Based on this comparison, a determination may be made as to whether the sensor has degraded.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/892,684, filed Feb. 9, 2018, which is a continuation of U.S.patent application Ser. No. 15/415,037, filed Jan. 25, 2017, which is acontinuation of U.S. patent application Ser. No. 15/018,205, filed Feb.8, 2016, now issued as U.S. Pat. No. 9,594,379 issued on Mar. 14, 2017,which is a continuation of U.S. patent application Ser. No. 13/630,054,filed Sep. 28, 2012, now issued as U.S. Pat. No. 9,274,525 issued onMar. 1, 2016, the disclosure of which is incorporated herein byreference.

BACKGROUND

An autonomous vehicle may use various computing systems to aid in thetransport of passengers from one location to another. In addition, theautonomous vehicle may require an initial input or continuous input froman operator, such as a pilot, driver, or passenger. Other autonomoussystems, for example autopilot systems, may be used only when the systemhas been engaged, which permits the operator to switch from a manualmode (where the operator exercises a high degree of control over themovement of the autonomous vehicle) to an autonomous mode (where theautonomous vehicle essentially drives itself) to modes that liesomewhere in between.

The autonomous vehicle may be equipped with various types of sensors inorder to detect objects in its environment. For example, the autonomousvehicles may include such sensors as lasers, sonar, radar, cameras, andother sensors that scan and record data from the autonomous vehicle'senvironment. Sensor data from one or more of these sensors may be usedto detect objects and their respective characteristics (position, shape,heading, speed, etc.). This detection and identification is a criticalfunction for the safe operation of the autonomous vehicle.

To navigate an environment confidently and precisely, the autonomousvehicle may rely on a prior stored electronic representation of theenvironment (e.g., a roadway, a highway, etc.). The electronicrepresentation of the environment may be considered a “map” thatidentifies such features as lane markings, lane edges, k-rail concretebarriers, lane dividers, road medians, traffic safety cones, and othersuch features. The autonomous vehicle may store the map for both complexand simple environments.

However, the sensors on the autonomous vehicle may experience one ormore problems, such as failure, inaccurate readings, or other suchproblems. When the sensor has a problem, the sensor may be unusable fordetermining whether there are objects, such as other vehicles, proximateto the autonomous vehicle. Having a sensor failure decreases the abilityof the autonomous vehicle to navigate its environment confidently andprecisely.

BRIEF SUMMARY

An apparatus and method are disclosed. In one embodiment, the apparatusincludes a computer-readable memory that stores a change in a movementcharacteristic of an autonomous vehicle, a sensor configured to detectan object in a driving environment of the autonomous vehicle, and aprocessor, in communication with the sensor and the computer-readablememory. The processor may be configured to receive a first sensorreading from the sensor based on a first detection of the object in thedriving environment, determine first state information for the objectbased on the received first sensor reading, change the movementcharacteristic of the autonomous vehicle based on the stored change inthe movement characteristic of the autonomous vehicle, and determineexpected state information based on the first state information and thechange in the movement characteristic of the autonomous vehicle. Theprocessor may also be configured to receive a second sensor reading fromthe sensor based on a second detection of the object in the drivingenvironment, determine second state information for the object based onthe received second sensor reading, and determine sensor degradation inthe sensor by comparing the second state information with the expectedstate information.

In another embodiment of the apparatus, the change in the movementcharacteristic of the autonomous vehicle comprises one of changing theautonomous vehicle's position, changing the autonomous vehicle's speed,or changing the autonomous vehicle's heading.

In a further embodiment of the apparatus, the sensor comprises one of acamera, a radar detection unit, or a laser sensor.

In yet another embodiment of the apparatus, the object is a movingobject.

In yet a further embodiment of the apparatus, the object is a staticobject.

In another embodiment of the apparatus, the processor is configured tochange the movement characteristic of the autonomous vehicle byincreasing a speed of the autonomous vehicle by a predetermined amount.

In a further embodiment of the apparatus, the processor is configured tochange the movement characteristic of the autonomous vehicle by changinga heading of the autonomous vehicle.

In yet another embodiment of the apparatus, the first state informationcomprises a distance from the object relative to the autonomous vehicle.

In yet a further embodiment of the apparatus, the processor is furtherconfigured to determine a deviation value from comparing the secondstate information with the expected state information, and the processordetermines sensor degradation in the sensor based on a comparison of thedetermined deviation value with a deviation threshold value.

In another embodiment of the apparatus, the processor is furtherconfigured to perform an action when the determined deviation valueexceeds the deviation threshold value.

In one embodiment of the method, the method includes receiving, with aprocessor in communication with a sensor, a first sensor reading fromthe based on a first detection of an object in a driving environment,determining, with the processor, first state information for the objectbased on the received first sensor reading, and changing a movementcharacteristic of the autonomous vehicle based on the stored change inthe movement characteristic of the autonomous vehicle. The method mayalso include determining, with the processor, expected state informationbased on the first state information and the change in the movementcharacteristic of the autonomous vehicle and receiving, with theprocessor, a second sensor reading from the sensor based on a seconddetection of the object in the driving environment. The method mayfurther include determining, with the processor, second stateinformation for the object based on the received second sensor reading,and determining, with the processor, sensor degradation in the sensor bycomparing the second state information with the expected stateinformation.

In another embodiment of the method, the change in the movementcharacteristic of the autonomous vehicle comprises one of changing theautonomous vehicle's position, changing the autonomous vehicle's speed,or changing the autonomous vehicle's heading.

In a further embodiment of the method, the sensor comprises one of acamera, a radar detection unit, or a laser sensor.

In yet another embodiment of the method, the object is a moving object.

In yet a further embodiment of the method, the object is a staticobject.

In another embodiment of the method, the method further includeschanging the movement characteristic of the autonomous vehicle byincreasing a speed of the autonomous vehicle by a predetermined amount.

In a further embodiment of the method, the method includes changing themovement characteristic of the autonomous vehicle by changing a headingof the autonomous vehicle.

In yet another embodiment of the method, the first state informationcomprises a distance from the object relative to the autonomous vehicle.

In yet a further embodiment of the method, the method includesdetermining, with the processor, a deviation value from comparing thesecond state information with the expected state information, anddetermining, with the processor, sensor degradation in the sensor basedon a comparison of the determined deviation value with a deviationthreshold value.

In another embodiment of the method, the method includes performing anaction when the determined deviation value exceeds the deviationthreshold value.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 illustrates an example of an autonomous vehicle configured todetect sensor degradation in one or more sensors of the autonomousvehicle according to aspects of the disclosure.

FIG. 2 illustrates an example of an interior of the autonomous vehicleaccording to aspects of the disclosure

FIG. 3 illustrates an example of the placement of the one or moresensors of the autonomous vehicle according to aspects of thedisclosure.

FIGS. 4A-4D illustrates various views of the approximate sensor fieldsof the various sensors of the autonomous vehicle according to aspects ofthe disclosure.

FIG. 5 illustrates an example of detailed map information that may bestored by the autonomous vehicle in accordance with aspects of thedisclosure.

FIG. 6 illustrates an example of the autonomous vehicle obtaininginitial sensor readings from a radar detection unit according to aspectsof the disclosure.

FIG. 7 illustrates an example of the autonomous vehicle obtaining sensorreadings from a radar detection unit after changing the direction oftravel for the autonomous vehicle according to aspects of thedisclosure.

FIG. 8 illustrates an example of the autonomous vehicle obtaining sensorreadings from a laser sensor according to aspects of the disclosure.

FIG. 9 illustrates an example of the autonomous vehicle obtaining sensorreadings from the laser sensor after changing the direction of travelfor the autonomous vehicle according to aspects of the disclosure.

FIG. 10 illustrates an example of logic flow for determining sensordegradation for one or more sensors of the autonomous vehicle accordingto aspects of the disclosure.

DETAILED DESCRIPTION

This disclosure provides for systems and methods for determining sensordegradation in one or more sensor of an autonomous vehicle by activelycontrolling the autonomous vehicle. In particular, this disclosureprovides for introducing computer-controlled movements into the motionof the autonomous vehicle and then determining whether the sensors ofthe autonomous vehicle provide sensor readings that correlate to thosemovements.

In one embodiment, the autonomous vehicle may introduce these movementsto determine whether one or more sensors are operating within normal orpreferred parameters by comparing the sensor readings of the one or moresensors with other moving objects (e.g., vehicles proximate to theautonomous vehicle). For example, the autonomous vehicle may beconfigured to test a radar detection unit in this manner. Specifically,if the radar detection unit detects a vehicle in front of the autonomousvehicle and the autonomous vehicle is turned slightly to the left orright, the computing systems on the autonomous vehicle would expect thatthe lateral speed of the corresponding radar target for the observedvehicle would move in the opposite direction (i.e., right or left)corresponding to the slight turn of the autonomous vehicle, with amagnitude relative to the distance between the observed vehicle and theautonomous vehicle.

To confirm that the radar detection unit is operating within normal orpreferred operational parameters, a small perturbation may be introducedor added to the autonomous vehicle yaw rate (e.g., a slight turn of thesteering wheel). The computing systems of the autonomous vehicle maythen check if the lateral speed of observed vehicles returned by theradar detection unit is within a range of expected lateral speeds of theobserved vehicles. If the observed lateral speed(s) are not within therange of expected lateral speed(s), then the computing systems of theautonomous vehicle may identify sensor degradation or determine that theradar detection unit is operating outside of normal or preferredoperating parameters.

The autonomous vehicle may also be configured to perform these testsusing non-moving features of the driving environment. For example, theautonomous vehicle may be configured to perform these tests usinglaser-based sensors. In this regard, the autonomous vehicle may bemaneuvered into areas where the stored map of the driving environmenthas a high degree of accuracy in the geometry of the driving environmentor laser intensity values, and the computing systems of the autonomousvehicle may be configured confirm that the sensor readings of the lasersensor match the geometry and/or laser intensity values. In this regard,the computing systems of the autonomous vehicle may be configured tocompare the laser sensor readings with static features of the drivingenvironment, such as lane markers, k-rail concrete barriers, or otherstatic features. In addition, certain features, such as k-rail concretebarriers or billboards, may be used to compare geometry readings fromthe laser sensor, whereas other features, such as lane reflectors orlane markers, may be used to compare intensity values from the lasersensor. In addition, the laser sensor may be evaluated as a whole (e.g.,the laser sensor is operating within expected parameters) and/or eachbeam of the laser sensor may be evaluated individually.

FIG. 1 illustrates an autonomous vehicle 102 configured to determinesensor degradation in one or more of its sensors. The autonomous vehicle102 may be configured to operate autonomously, e.g., drive without theassistance of a human driver. Moreover, the autonomous vehicle 102 maybe configured to detect various objects and determine whether thedetected object is a vehicle.

While certain aspects of the disclosure are particularly useful inconnection with specific types of vehicles, the autonomous vehicle 102may be any type of vehicle including, but not limited to, cars, trucks,motorcycles, busses, boats, airplanes, helicopters, lawnmowers,recreational vehicles, amusement park vehicles, farm equipment,construction equipment, trams, golf carts, trains, and trolleys.

In one embodiment, the autonomous driving computer system 144 mayinclude a processor 106 and a memory 108. The autonomous drivingcomputer system 144 may also include other components typically presentin a general purpose computer.

The memory 108 may store information accessible by the processor 106,such as instructions 110 and data 112 that may be executed or otherwiseused by the processor 106. The memory 108 may be of any type of memoryoperative to store information accessible by the processor 106,including a computer-readable medium, or other medium that stores datathat may be read with the aid of an electronic device. Examples of thememory 108 include, but are not limited, a hard-drive, a memory card,read-only memory (“ROM”), random-access memory (“RAM”), digital videodisc (“DVD”), or other optical disks, as well as other write-capable andread-only memories. Systems and methods may include differentcombinations of the foregoing, whereby different portions of theinstructions and data are stored on different types of media.

The instructions 110 may be any set of instructions that may be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor 106. For example, the instructions 110 may be stored ascomputer code on the computer-readable medium. In that regard, the terms“instructions” and “programs” may be used interchangeably herein. Theinstructions 110 may be stored in object code format for directprocessing by the processor 106, or in any other computer languageincluding scripts or collections of independent source code modules thatare interpreted on demand or compiled in advance. Functions, methods androutines of the instructions 110 are explained in more detail below.

The data 112 may be retrieved, stored, or modified by processor 106 inaccordance with the instructions 110. For instance, although thedisclosed embodiments not limited by any particular data structure, thedata 112 may be stored in computer registers, in a relational databaseas a table having a plurality of different fields and records, XMLdocuments, flat files, or in any computer-readable format. By furtherway of example only, image data may be stored as bitmaps comprised ofgrids of pixels that are stored in accordance with formats that arecompressed or uncompressed, lossless (e.g., BMP) or lossy (e.g., JPEG),and bitmap or vector-based (e.g., SVG), as well as computer instructionsfor drawing graphics. The data 112 may comprise any informationsufficient to identify the relevant information, such as numbers,descriptive text, proprietary codes, references to data stored in otherareas of the same memory or different memories (including other networklocations) or information that is used by a function to calculate therelevant data.

The processor 106 may be any conventional processor, including ReducedInstruction Set Computing (“RISC”) processors, Complex Instruction SetComputing (“CISC”) processors, or combinations of the foregoing.Alternatively, the processor may be a dedicated device such as anapplicant-specific integrated circuit (“ASIC”).

Although FIG. 1 functionally illustrates the processor 106, the memory108, and other elements of the autonomous driving computer system 144 asbeing within the same block, it will be understood by those of ordinaryskill in the art that the processor 106 and the memory 108 may actuallycomprise multiple processors and memories that may or may not be storedwithin the same physical housing. For example, the memory 108 may be ahard drive or other storage media located in a housing different fromthat of the autonomous driving computer system 144.

Accordingly, references to a processor or computer will be understood toinclude references to a collection of processors or computers ormemories that may or may not operate in parallel. Rather than using asingle processor to perform the acts described herein, some of thecomponents, such as steering components and deceleration components, mayeach have their own processor that only performs calculations related tothe component's specific function.

In various embodiments described herein, the processor 106 may belocated remotely from the autonomous vehicle 102 and may communicatewith the autonomous vehicle wirelessly. In other aspects, some of theprocesses described herein are executed on a processor disposed withinthe autonomous vehicle 102 and others by a remote processor, includingtaking the acts necessary to execute a single maneuver.

The autonomous driving computer system 144 may include all of thecomponents normally used in connection with a computer, such as acentral processing unit (CPU), a memory (e.g., RAM and internal harddrives) storing data 112 and instructions such as an Internet browser orother software application, an electronic display 122 (e.g., a monitorhaving a screen, a small liquid crystal display (“LCD”) touch-screen orany other electrical device that is operable to display information),one or more user input devices (e.g., a mouse, keyboard, touch screenand/or microphone), as well as various sensors (e.g., a video camera)for gathering the explicit (e.g., a gesture) or implicit (e.g., “theperson is asleep”) information about the states and desires of a person.

The autonomous vehicle 102 may also include a geographic positioncomponent 136 in communication with the autonomous driving computersystem 144 for determining the geographic location of the autonomousvehicle 102. For example, the geographic position component 136 mayinclude a Global Positioning System (“GPS”) receiver to determine theautonomous vehicle's 102 latitude, longitude and/or altitude position.Other location systems such as laser-based localization systems,inertial-aided GPS, or camera-based localization may also be used toidentify the location of the vehicle. The location of the autonomousvehicle 102 may include an absolute geographical location, such aslatitude, longitude, and altitude as well as relative locationinformation, such as location relative to other vehicles immediatelyaround it which can often be determined with less noise than absolutegeographical location.

The geographic position component 136 may also include other devices incommunication with the autonomous driving computer system 144, such asan accelerometer, gyroscope or another direction/speed detection device138 to determine the direction and speed of the vehicle or changesthereto. By way of example only, the geographic position component 136may determine its pitch, yaw or roll (or changes thereto) relative tothe direction of gravity or a plane perpendicular thereto. Thegeographic position component 136 may also track increases or decreasesin speed and the direction of such changes. The location and orientationdata as set forth herein may be provided automatically to the user, theautonomous driving computer 144, the vehicle central processor 126,other computers and combinations of the foregoing.

The autonomous driving computer system 144 may control the direction andspeed of the autonomous vehicle 102 by controlling various components.By way of example, if the autonomous vehicle 102 is operating in acompletely autonomous mode, the autonomous driving computer system 144may cause the autonomous vehicle 102 to accelerate via the accelerationsystem 130 (e.g., by increasing fuel or other energy provided to theengine), decelerate via the braking system 128 (e.g., by decreasing thefuel supplied to the engine or by applying brakes) and change direction(e.g., by turning the front two wheels). The autonomous driving computersystem 144 may also control one or more systems, such as the signalingsystem 130, when controlling the acceleration system 130 and/or thebraking system 128.

The autonomous driving computer system 144 may also control one or morestatus indicators 118, which may convey the status of the autonomousvehicle 102 and its components to a passenger. For example, theautonomous vehicle 102 may be equipped with an electronic display 122for displaying information relating to the overall status of thevehicle, particular sensors, or information about or from the autonomousdriving computer system 144. The electronic display 122 may displaycomputer-generated images of the vehicle's surroundings including, forexample, the status of the autonomous driving computer system 144, theautonomous vehicle 102 itself, roadways, intersections, as well as otherobjects and information.

The autonomous driving computer system 144 may use visual or audiblecues to indicate whether it is obtaining valid data from one or moresensors, whether a passenger or the autonomous driving computer system144 is partially or completely controlling the direction or speed of theautonomous vehicle 102 or both, such as whether there are any errors,etc. In addition, the autonomous driving computer system 144 may alsohave external indicators which indicate whether, at the moment, a humanor an automated system is in control of the vehicle, that are readableby humans, other computers, or both.

The autonomous driving computer system 144 may also communicate withother components of the autonomous vehicle 102. For example, theautonomous driving computer system 144 may communicate with a vehiclecentral processor 126. The autonomous driving computer system 144 mayalso send and receive information from the various systems of theautonomous vehicle 102. Communicating with the various systems mayinclude communicating with the braking system 128, the accelerationsystem 130, the signaling system 132, and the vehicle navigation system134. Communications with these systems may facilitate the control of themovement, speed, etc. of the autonomous vehicle 102. In addition, whenengaged, autonomous driving computer system 144 may control some or allof these functions of the autonomous vehicle 102 and thus be fully orpartially autonomous. Although various systems and the autonomousdriving computer system 144 are shown within the autonomous vehicle 102,these systems and components may be external to the autonomous vehicle102 or physically separated by large distances.

The autonomous vehicle 102 may also include one or more sensors 146 fordetecting objects external to it, such as other vehicles, obstacles inthe roadway, traffic signals, signs, trees, etc. The sensors 146 mayinclude lasers, sonar, radar, cameras or any other detection devices.The lasers may include commercially available lasers such as theVelodyne HDL-64 or other models. For example, where the autonomousvehicle 102 is a small passenger car, the small passenger car mayinclude a laser mounted on the roof or other convenient location. In oneaspect, the laser may measure the distance between the autonomousvehicle 102 and the object surfaces facing the autonomous vehicle 102 byspinning on its axis and changing its pitch.

The autonomous vehicle 102 may also include various radar detectionunits, such as those used for adaptive cruise control systems. The radardetection units may be located on the front and back of the car as wellas on either side of the front bumper. In another example, a variety ofcameras may be mounted on the autonomous vehicle 102 at known distancesfrom one another. In this manner, the parallax from the different imagesmay be used to compute the distance to various objects captured by theone or more cameras. These sensors may assist the vehicle in respondingto its environment to maximize safety for passengers as well as objectsor people in the environment.

FIG. 3 illustrates one example of the autonomous vehicle 102 and theplacement of its one or more sensors. The autonomous vehicle 102 mayinclude lasers 302 and 304, for example, mounted on the front and top ofthe autonomous vehicle 102, respectively. The laser 302 may have a rangeof approximately 150 meters, a thirty degree vertical field of view, andapproximately a thirty degree horizontal field of view. The laser 304may have a range of approximately 50-80 meters, a thirty degree verticalfield of view, and a 360 degree horizontal field of view. The lasers302-304 may provide the autonomous vehicle 102 with range and intensityinformation that the processor 106 may use to identify the location anddistance of various objects. In one aspect, the lasers 302-304 maymeasure the distance between the vehicle and object surfaces facing thevehicle by spinning on its axes and changing their pitch. Other laserswith different ranges and fields of view may also be used.

The autonomous vehicle 102 may also include various radar detectionunits, such as those used for adaptive cruise control systems. The radardetection units may be located on the front and back of the car as wellas on either side of the front bumper. As shown in the example of FIG.3, the autonomous vehicle 102 includes radar detection units 306-312located on the side (only one side being shown), front and rear of thevehicle. Each of these radar detection units 306-312 may have a range ofapproximately 200 meters for an approximately 18 degree field of view aswell as a range of approximately 60 meters for an approximately 56degree field of view. Again other radar detection units with differentranges and fields of view may also be used.

In another example, a variety of cameras may be mounted on theautonomous vehicle 102. The cameras may be mounted at predetermineddistances so that the parallax from the images of two or more camerasmay be used to compute the distance to various objects. As shown in FIG.3, the autonomous vehicle 102 may include two cameras 314-316 mountedunder a windshield 318 near the rear view mirror (not shown). The camera314 may include a range of approximately 200 meters and an approximately30 degree horizontal field of view, while the camera 316 may include arange of approximately 100 meters and an approximately 60 degreehorizontal field of view. Other cameras with different ranges and fieldsof view may also be used.

Each sensor may be associated with a particular sensor field defined bythe ranges and fields of view for which the sensor may be used to detectobjects. FIG. 4A is a top-down view of the approximate sensor fields ofthe various sensors. FIG. 4B depicts the approximate sensor fields 402and 404 for the lasers 302 and 304, respectively based on the fields ofview for these sensors. In this example, the sensor field 402 includesan approximately 30 degree horizontal field of view for approximately150 meters, and the sensor field 404 includes a 360-degree horizontalfield of view for approximately 80 meters.

FIG. 4C depicts the approximate sensor fields 406-420 and for radardetection units 306-312, respectively, based on the fields of view forthese sensors. For example, the radar detection unit 306 includes sensorfields 406 and 408. The sensor field 406 includes an approximately 18degree horizontal field of view for approximately 200 meters, and thesensor field 408 includes an approximately 56 degree horizontal field ofview for approximately 80 meters.

Similarly, the radar detection units 308-312 include the sensor fields410/414/418 and sensor fields 412/416/420. The sensor fields 410/414/418include an approximately 18 degree horizontal field of view forapproximately 200 meters, and the sensor fields 412/416/420 include anapproximately 56 degree horizontal field of view for approximately 80meters. The sensor fields 410 and 414 extend passed the edge of FIGS. 4Aand 4C.

FIG. 4D depicts the approximate sensor fields 422-424 of cameras314-316, respectively, based on the fields of view for these sensors.For example, the sensor field 422 of the camera 314 includes a field ofview of approximately 30 degrees for approximately 200 meters, andsensor field 424 of the camera 316 includes a field of view ofapproximately 60 degrees for approximately 100 meters.

In general, an autonomous vehicle 102 may include sonar devices, stereocameras, a localization camera, a laser, and a radar detection unit eachwith different fields of view. The sonar may have a horizontal field ofview of approximately 60 degrees for a maximum distance of approximately6 meters. The stereo cameras may have an overlapping region with ahorizontal field of view of approximately 50 degrees, a vertical fieldof view of approximately 10 degrees, and a maximum distance ofapproximately 30 meters. The localization camera may have a horizontalfield of view of approximately 75 degrees, a vertical field of view ofapproximately 90 degrees and a maximum distance of approximately 10meters. The laser may have a horizontal field of view of approximately360 degrees, a vertical field of view of approximately 30 degrees, and amaximum distance of 100 meters. The radar may have a horizontal field ofview of 60 degrees for the near beam, 30 degrees for the far beam, and amaximum distance of 200 meters. Hence, the autonomous vehicle 102 may beconfigured with any arrangement of sensors having different fields ofview, ranges, and sensor fields, and each of these sensors may captureone or more raw images for use by the object detector 130 to detect thevarious objects near and around the autonomous vehicle 102.

In addition to the sensors 146 described above, the autonomous drivingcomputer system 144 may also use input from sensors found innon-autonomous vehicles. As examples, these sensors may include tirepressure sensors, engine temperature sensors, brake heat sensors, breakpad status, tire tread sensors, fuel sensors, oil level and qualitysensors, air quality sensors (for detecting temperature, humidity, orparticulates in the air), etc.

The data provided by the sensors 146 may be processed by the autonomousdriving computer system 144 in real-time. In this context, the sensors146 may continuously update their output to reflect the environmentbeing sensed at or over a range of time, and continuously or asdemanded. The sensors 146 may provide the updated output to theautonomous driving computer system 144 so that it can determine whetherthe autonomous vehicle's 102 then-current direction or speed should bemodified in response to the sensed environment.

Referring back to FIG. 1, the autonomous vehicle 102 may also include anelectronic representation of a driving environment for maneuvering inthe driving environment, and for determining whether there are one ormore objects proximate to the autonomous vehicle 102 in the drivingenvironment. For example, autonomous driving computer system 144 mayinclude detailed map information 114 that defines one or more drivingenvironments. The detailed map information 114 may include various mapsthat identify the shape and elevation of roadways, lane lines,intersections, crosswalks, speed limits, traffic signals, buildings,signs, real time traffic information, or other such objects andinformation. The detailed map information 114 may further includeexplicit speed limit information associated with various roadwaysegments. The speed limit data may be entered manually or scanned frompreviously taken images of a speed limit sign using, for example,optical-character recognition. In addition, the detailed map information114 may include three-dimensional terrain maps incorporating one or moreof the objects (e.g., crosswalks, intersections, lane lines, etc.)listed above.

The detailed map information 114 may also include lane markerinformation identifying the location, elevation, and shape of lanemarkers. The lane markers may include features such as solid or brokendouble or single lane lines, solid or broken lane lines, reflectors,etc. A given lane may be associated with left and right lane lines orother lane markers that define the boundary of the lane. Thus, mostlanes may be bounded by a left edge of one lane line and a right edge ofanother lane line.

The autonomous vehicle 102 may also include persistent data fordetecting objects and determining whether one or more of the sensor isexperiencing (has experienced) degradation. For example, the data 112may include one or more sensor parameters 148 used by the processor 106to determine when a sensor has detected an object, and the type ofobject detected by the sensor. More particularly, the sensor parameters148 may define an arrangement of pixels, laser points, intensity maps,etc., that should be considered an object. The sensor parameters 148 mayalso define how an object is to be classified (e.g., “vehicle,”“pedestrian,” “k-rail concrete barrier,” etc.).

Each of the sensors 146 of the autonomous vehicle 102 may be associatedwith a corresponding set of the sensor parameters 148. Thus, the one ormore camera(s) may be associated with camera parameters, the one or morelaser(s) may be associated with laser parameters, and the one or moreradar detection unit(s) may be associated with radar parameters.Examples of camera parameters may include the minimal brightness of apedestrian, the minimum pixel size of a car object, the minimum width ofa car object, and other such parameters. Examples of laser parametersmay include the height of a pedestrian, the length of a car object, anobstacle detection threshold, and other such parameters. Examples ofradar parameters may include minimum distance to an object, a delaythreshold for detecting an object, the velocity of a detected object,the height of a pedestrian, and other such parameters.

In detecting vehicles in various driving environments, the data 112 mayinclude vehicle data 116 that defines one or more parameters forclassifying a vehicle. Classifications of vehicle may include suchclassifications as “passenger car,” “bicycle,” “motorcycle,” and othersuch classifications. The parameters defined by the vehicle data 116 mayinform the autonomous driving computer system 144 as to the type ofvehicle detected by a given sensor. For example, the vehicle data 116may include parameters that define the type of vehicle when the vehicleis detected by one or more of the camera sensors, one or more of thelaser sensors, and so forth.

Vehicles may be identified through a vehicle detection algorithm 124,which the processor 106 may use to classify vehicles based on variouscharacteristics, such as the size of the vehicle (bicycles are largerthan a breadbox and smaller than a car), the speed of the vehicle(bicycles do not tend to go faster than 40 miles per hour or slower than0.1 miles per hour), and other such characteristics. In addition, thevehicle may be classified based on specific attributes of the vehicle,such as information contained on a license plate, bumper sticker, orlogos that appear on the vehicle.

The vehicle data 116 may also include state, positional, and/ortrajectory information collected by the autonomous driving computersystem 144 when a vehicle is detected. The autonomous driving computersystem 144 may collect the state and/or positional information about adetected object such as a vehicle to assist in the determination of thedetected vehicle's trajectory. The vehicle trajectory for the detectedvehicle may define the direction and speed that a vehicle has when in agiven driving environment. The vehicle trajectory may also define thepast positions, directions, and speed that the detected vehicle hadwhile in the driving environment.

The autonomous vehicle 102 may generate state information about detectedobjects such as other vehicles regardless of whether the autonomousvehicle 102 is operating in an autonomous mode or a non-autonomous mode.Thus, whether the autonomous vehicle 102 is operating by itself or has adriver, the autonomous vehicle 102 may collect state and objectinformation, for example, in order to determine the aforementionedvehicle trajectories.

The instructions 110 may also include a sensor degradation detectionalgorithm 124 for identifying sensor degradation in one or more of thesensors 146. As previously discussed, and described in more detailbelow, determining whether a sensor is experiencing (or has experienced)degradation may include comparing observed sensor readings (e.g.,position, velocity, trajectory, etc.) of moving objects with expectedsensor readings of those moving objects and/or comparing observed sensorreadings (e.g., geometry, intensity values, etc.) of static objects withexpected sensor readings of those static objects.

FIG. 2 illustrates an example of an interior of the autonomous vehicle102 according to aspects of the disclosure. The autonomous vehicle 102may include all of the features of a non-autonomous vehicle, forexample: a steering apparatus, such as steering wheel 210; a navigationdisplay apparatus, such as navigation display 215; and a gear selectorapparatus, such as gear shifter 220. The vehicle 102 may also havevarious user input devices, such as gear shifter 220, touch screen 217,or button inputs 219, for activating or deactivating one or moreautonomous driving modes and for enabling a driver or passenger 290 toprovide information, such as a navigation destination, to the autonomousdriving computer 106.

The autonomous vehicle 102 may also include one or more additionaldisplays. For example, the autonomous vehicle 102 may include a display225 for displaying information regarding the status of the autonomousvehicle 102 or its computer. In another example, the autonomous vehicle102 may include a status indicating apparatus such as status bar 230, toindicate the current status of vehicle 102. In the example of FIG. 2,the status bar 230 displays “D” and “2 mph” indicating that theautonomous vehicle 102 is presently in drive mode and is moving at 2miles per hour. In that regard, the autonomous vehicle 102 may displaytext on an electronic display, illuminate portions of the autonomousvehicle 102, such as the steering wheel 210, or provide various othertypes of indications.

FIG. 5 illustrates an example of a portion of a detailed map 502 thatmay represent the driving environment of the autonomous vehicle 102. Thedetailed map 502 may be retrieved or referenced by the autonomousvehicle 102 from the detailed map information 114 based on a detectedposition of the autonomous vehicle 102. The detailed map 502 may bestored as part of the detailed map information 114.

The detailed map 502 may further represent a section of a road, such ashighway, parkway, etc., and may include lane information such asinformation about solid lane lines 504-508 and broken lane lines510-512. These lane lines may define lanes 514-520. Each of the lanes514-520 may be associated with a respective centerline rail 522-528which may indicate the direction in which a vehicle should generallytravel in the respective lane. For example, a vehicle may followcenterline rail 522 when driving in lane 514. In this example, the lane514 may be bounded by a left lane line 504 and the right lane line 506.Similarly, the lane 516 may be bounded by the left lane line 506 and theright lane line 508, the lane 518 may be bounded by the left lane line508 and the right lane line 510, and the lane 520 may be bounded by theleft lane line 510 and the right lane line 512.

Each of the lanes 514-520 may be bounded by corresponding lane edges.Thus, lane 514 may be bounded by edges 530,532, lane 516 may be boundedby edges 534,536, lane 518 may be bounded by edges 538,540 and lane 520may be bounded by edges 542,544.

In the example shown in FIG. 5, the detailed map information 114 may bedepicted as an image-based map. However, the detailed map information114 need not be entirely or completely image-based (e.g., raster-based).For example, the detailed map information 114 may include one or moreroadgraphs or graph networks of information such as roads, lanes,intersections, and the connections between these features. Each featuremay be stored as graph data and may be associated with information suchas a geographic location and whether or not it is linked to otherrelated features, for example, a stop sign may be linked to a road andan intersection, etc. In some examples, the associated data may includegrid-based indices of a roadgraph to allow for efficient lookup ofcertain roadgraph features.

The detailed map information 114, such as that depicted in FIG. 5, maybe loaded into the memory 108 of the autonomous vehicle 102 at apredetermined time. In one embodiment, the detailed map information 114may be loaded into the memory 108 of the autonomous vehicle 102 on adaily basis. Alternatively, or in addition, the detailed map information114 may be loaded into the memory 108 at other predetermined times, suchas on a monthly or weekly basis.

As previously discussed, the autonomous driving computer system 144 mayinclude instructions 110 having various algorithms for detecting andidentifying objects in a driving environment and for determining whetherone or more of the sensors 126 is experiencing, or has experienced,sensor degradation based on the detected objects.

FIGS. 6-7 illustrate examples 602,702 of the autonomous vehicle 102using non-static (i.e., moving) objects in a driving environment toidentify sensor degradation or to determine whether a sensor isoperating outside of normal or preferred operating parameters. In FIG.6, the autonomous vehicle 102 may obtain initial sensor readings 610-614from one or more detected vehicles 604-608 in a driving environment. Thedetailed map 502 may correspond to the driving environment of FIG. 6. Inthe example 602, the autonomous vehicle 102 may detect and identify oneor more vehicles, such as vehicles 604-608, using the vehicle detectionalgorithm 124. The vehicles 604-608 may be within range of one or moreof the sensor fields projected by the autonomous vehicle 102, such asthe radar sensor field 408. The vehicles 604-608 may be within range ofother sensor fields, but these other sensor fields have been omitted forsimplicity and clarity.

In the example 602, the vehicles 604-608 and the autonomous vehicle 102may be in various lanes of the driving environment. In particular, thevehicle 604 may be in the lane 514, the vehicle 606 may be in the lane516, and the vehicle 608 may be in the lane 520. The autonomous vehicle102 may be in the lane 516.

The autonomous driving computer system 144 may receive data from sensorsone or more object in the autonomous vehicle's 102 surroundings. Forexample, the autonomous driving computer system 144 may receive dataregarding the vehicles 604-608 from the radar detection unit 306(corresponding to the radar sensor field 408 as shown in FIG. 6). Thedata received from the vehicles 604-608 may include sensor readings 610corresponding to the vehicle 604, sensor readings 612 from the vehicle606, and sensor readings 614 corresponding to the vehicle 608.

The sensor readings 610-614 received by the radar detection unit 306 maybe used to establish baseline sensor readings for each of the vehicles604-608. The autonomous driving computer system 144 may use the baselinesensor readings to determine baseline state information for one or moreof the vehicles 604-608 such as an initial distance from the autonomousvehicle 102, an initial trajectory (e.g., direction of travel and speedof travel), an initial speed (either relative to the autonomous vehicle102 and/or relative to the driving environment), and other such stateinformation.

After determining the baseline state information for the one or morevehicles 604-608, the autonomous driving computer system 144 may thenchange one or more movement characteristics of the autonomous vehicle102 by a predetermined amount.

FIG. 7 illustrates an example 702 of the autonomous driving computersystem 144 changing the one or more movement characteristics of theautonomous vehicle 102. The change in the movement characteristics ofthe autonomous vehicle 102 may include changes to such movementcharacteristics as speed, heading, acceleration, deceleration, the yawrate in which the steering wheel of the autonomous vehicle 102 isturned, and other such changes in the movement characteristics of theautonomous vehicle 102.

Changes in the movements of the autonomous vehicle 102 may be based onvarious predetermined amounts. For example, changes in speed may includeincreasing or decreasing the speed of travel by the autonomous vehicle102 by one or two miles per hour (“MPH”), changes in acceleration ordeceleration may include increasing or decreasing the speed of theautonomous vehicle 102 by a predetermined rate, such as 3.4 m/s²,changes in the yaw rate may include turning the steering of the vehicleby 0.75 deg/sec, 3 deg/sec, and other similar amounts.

The autonomous driving computer system 144 may obtain second sensorreadings 704-708 for the one or more autonomous vehicles 604-608 after,or during, the changes in movements to the autonomous vehicle 102. Theautonomous driving computer system 144 may then determine second stateinformation for one or more of the vehicles 604-608 based on the secondsensor readings 704-708. The second state information determined fromthe second sensor readings 704-708 may include one or more of theparameters determined from the baseline state information. In otherwords, the second state information may include such information asdistance from the autonomous vehicle 102, trajectory (e.g., direction oftravel and speed of travel), speed (either relative to the autonomousvehicle 102 and/or relative to the driving environment), and other suchstate information.

The second state information may be compared with expected stateinformation determined by the autonomous driving computer system 144.Expected state information may include state information that theautonomous driving computer system 144 would expect from the one or morevehicles 604-608 based on changes the movement characteristic(s) of theautonomous vehicle 102. For example, all other parameters remaining thesame, should the speed of the autonomous vehicle 102 decrease, theautonomous driving computer system 144 would expect that the distancebetween the autonomous vehicle 102 and the previously detected vehicles604-608 would increase by an amount corresponding to the decrease inspeed of the autonomous vehicle 102.

The autonomous driving computer system 144 may determine the expectedstate information for the one or more vehicles 604-608. Thedetermination of the expected state information may be based on thebaseline state information for each of the one or more vehicles 604-608and the changes in the movement characteristics of the autonomousvehicle 102. For example, where the baseline state information includesan initial distance, such as the initial distance of the vehicle 604,from the autonomous vehicle 102, and the change in the movementcharacteristics of the autonomous vehicle 102 include changing the speedof the autonomous vehicle 102, the expected state information mayinclude an expected distance of the vehicle 604 from the autonomousvehicle 102 determined based on the initial distance of the vehicle 604and the change in speed of the autonomous vehicle 102. As anotherexample, where the baseline state information includes an initial speed,such as an initial speed of the vehicle 604, and the change in themovement characteristics of the autonomous vehicle 102 include changingthe speed of the autonomous vehicle 102, the expected state informationmay include an expected speed of the vehicle 604 based on the initialspeed of the vehicle 604 and the change in speed of the autonomousvehicle 102.

By comparing the second state information with the expected stateinformation, the autonomous driving computer system 144 may determineone or more deviation values. For example, where the second stateinformation and expected state information include distance, speed,trajectory, and so forth, there may be a corresponding distancedeviation value, speed deviation value, trajectory deviation value, etc.Furthermore each of the values may have a magnitude and sign. Forexample, where an expected distance value of the vehicle 604 is 20meters, and the determined distance value of the vehicle 604 (based onthe second state information) is 10 meters, the distance deviation valuemay be “−10 meters.” In this example, the distance deviation valueindicates that the radar detection unit is detecting the vehicle 604closer to the autonomous vehicle 102 than the autonomous drivingcomputer system 144 predicted.

Moreover, the autonomous driving computer system 144 may establish oneor more deviation threshold values for the various types of stateinformation. One or more of the deviation threshold values may be storedin the data 112.

Accordingly, there may be a distance deviation threshold value (e.g.,+/−2 meters), a speed deviation threshold value (e.g., +/−2 MPH), a yawrate deviation threshold value (e.g., +/−0.4 deg/sec), and other suchthreshold values. Where one or more of the determined deviation valuesexceed a corresponding deviation threshold value of the same type, theautonomous driving computer system 144 may identify sensor degradationin the sensor (i.e., the radar detection unit 306) or determine that thesensor is operating outside of normal or preferred operationalparameters. In other words, the autonomous driving computer system 144may determine that the sensor (i.e., the radar detection unit 306) isfaulty.

In addition, depending on the severity or magnitude of the difference inthe deviation threshold value and the corresponding determined deviationvalue, or the number of deviation threshold values that are exceeded,the autonomous driving computer system 144 may perform a variety ofactions. For example, where one deviation threshold value is exceeded,the autonomous driving computer system 144 may display a warning to apassenger. Where two deviation threshold value types are exceeded (e.g.,distance and speed, yaw and distance, etc.), the autonomous drivingcomputer system 144 may request that a passenger take control of theautonomous vehicle 102. Similarly, where a difference in a deviationthreshold value and the corresponding determined deviation value exceedsa predetermined amount, the autonomous driving computer system 144 maydisplay a warning, request a passenger take control, and other actionsor combination of actions.

Furthermore, the autonomous driving computer system 144 may correlatethe identification of sensor degradation with the detailed mapinformation 114 to determine where or when the sensor may have begunoperating outside of normal or preferred operating parameters. Forexample, the autonomous driving computer system 144 may record the time(e.g., “2:43 P.M,” “1450”, etc.) and/or place (e.g., latitude/longitude)where it identifies sensor degradation in a sensor (i.e., one or moredeviation values exceed one or more deviation thresholds). Based on thisrecordation of time and/or place, the detailed map information 114 maybe cross-referenced to determine if there were any environmental factors(e.g., speed bumps, known potholes, known construction, etc.) that mayhave affected the operations of the faulty sensor.

In another embodiment of determining sensor degradation, the autonomousvehicle 102 may reference one or more static objects in the drivingenvironment. FIG. 8 illustrates an example 802 of the autonomous vehicle102 determining whether a sensor, in this example, the laser sensor 304,is operating outside of normal or preferred operating parameters usingstatic features in the driving environment. In particular, theautonomous driving computer system 144 may leverage the detection of oneor more of the lane lines 504-512 and/or lane edges 530-544 indetermining whether there is sensor degradation in the laser sensor 304.

The example 802 illustrates various concentric circles emanating fromthe laser sensor 304 of the autonomous vehicle 102. Each of theconcentric circles may correspond to a series of data points generatedby a laser beam projected from the laser sensor 304. In addition, wherea laser beam strikes a lane line edge, the autonomous driving computersystem 144 may record the detection of the lane line edge as a laserpoint, including the position (latitude, longitude, and elevation) ofthe laser point, and the intensity of the reflection from the laserpoint along the lane line edge. In addition, a composite of the detectedpoints along the lane line edge may be formulated in order to determinethe geometry (width, height, length, etc.) of the lane line. Althoughthe example 802 illustrates detecting lane lines and lane line edges,the determination of geometry, position, and laser intensity may also beperformed with respect to other static objects in the drivingenvironment, such as billboards, k-rail concrete barriers, and othersuch static objects.

To determine whether the laser sensor 304 is operating within normal orpreferred operating parameters, the autonomous driving computer system144 may compare recorded laser points of the laser sensor 304 (e.g.,laser point positions, laser point intensities, etc.) with the laserpoints of the detailed map 502 of FIG. 5. Comparing recorded laserpoints with laser points of the detailed map 502 may include comparinglaser point positions (latitude and/or longitude, distance from theautonomous vehicle 102, etc.), light intensities, laser pointelevations, and other such information.

From this comparison, the autonomous driving computer system 144 mayobtain one or more map deviation values that indicate the number, orpercentage, of recorded laser points that are different from the laserpoints associated with the detailed map. For example, a comparison mayyield that %5 of the recorded laser points are different (in position,light intensity, elevation, etc.) from the laser points associated withthe detailed map. A comparison may also include more granularinformation, such as the number or percentage of laser points thatdiffer in position, elevation, light intensity, and so forth.

FIG. 9 illustrates an example 902 of obtaining a second set of recordedlaser points for use in identifying sensor degradation in the lasersensor 304. In particular, the example 902 illustrates obtaining thesecond set of recorded laser points based on changes in one or moremovement characteristics of the autonomous vehicle 102. In this regard,the autonomous driving computer system 144 may use the recorded laserpoints of FIG. 8 as a baseline set of recorded laser points, and then,based on changes in the movement characteristics of the autonomousvehicle 102 (e.g., speed, heading, position, etc.), determine anexpected set of laser points. The autonomous driving computer system 144may then compare the expected set of laser points (determined fromexample 802) with the second set of recorded laser points. Based on thiscomparison, the autonomous driving computer system 144 may determine anumber of movement deviation values, including a number or percentage ofthe laser points from the second set of recorded laser points that aredifferent from the expected set of laser points. Comparing the secondset of recorded laser points with the expected set of laser points mayinclude comparing position (e.g., latitude, longitude, elevation), lightintensity, and, where the autonomous driving computer system 144identifies an object from a group of laser points, geometry (e.g.,height, width, depth, etc.)

Having determined one or more map and/or movement deviation values(e.g., positional deviation values, light intensity deviation values,etc.), the autonomous driving computer system 144 may compare the mapand/or movement deviation values with one or more correspondingdeviation thresholds values. Thus, there may be a positional deviationthreshold values, an elevation deviation threshold value, a lightintensity threshold value, and other such threshold values. Where one ormore of the determined deviation values exceed the correspondingdeviation threshold values, the autonomous driving computer system 144may identify sensor degradation in the sensor (i.e., the laser sensor304) or determine that the sensor is operating outside of normal orpreferred operational parameters.

In addition, depending on the severity or magnitude of the difference ina deviation threshold value and a corresponding deviation value, or thenumber of deviation threshold values that are exceeded, the autonomousdriving computer system 144 may perform a variety of actions. Forexample, where one deviation threshold value is exceeded, the autonomousdriving computer system 144 may display a warning to a passenger. Wheretwo deviation threshold values are exceeded, the autonomous drivingcomputer system 144 may request that a passenger take control of theautonomous vehicle 102. Similarly, where a difference in a deviationthreshold value and the deviation value exceeds a predetermined amount,the autonomous driving computer system 144 may display a warning,request a passenger take control, and other actions or combination ofactions.

FIG. 10 illustrates an example of logic flow 1002 for determiningwhether a sensor is faulty or sensor degradation has occurred accordingto aspects of the disclosure. Initially, the autonomous driving computersystem 144 may detect one or more objects in a driving environment(Block 1004) of the autonomous vehicle 102. The detection may beperformed with one or more of the sensors of the autonomous vehicle 102,such as the radar detection unit 306, or other sensor.

The autonomous driving computer system 144 may then determine baselinestate information for the detected objects (Block 1006). The baselinestate information may include distance, position, heading, trajectory,speed, and other such state information.

The autonomous driving computer system 144 may then change one or moremovement characteristics of the autonomous vehicle 102 (Block 1008).Changing one or more of the movement characteristics of the autonomousvehicle 102 may include changes to speed, acceleration or decelerationrate, changes in heading, changes in the yaw rate, and other suchchanges.

The autonomous driving computer system 144 may then determine secondstate information for the detected objects (Block 1010). This mayinclude determining one or more corresponding parameter valuespreviously determined for the baseline state information.

The autonomous driving computer system 144 may also determine expectedstate information (Block 1012). Determining the expected stateinformation may be based on the baseline state information and thechanges to the movement characteristics of the autonomous vehicle 102.

The autonomous driving computer system 144 may then compare the expectedstate information with the determined second state information (Block1014). The comparison of the expected state information with thedetermined second state information may yield one or more deviationvalues. These deviation values may then be compared with one or morecorresponding deviation thresholds. The autonomous driving computersystem 144 may be configured to perform one or more actions (e.g.,display a warning, request a passenger take control, etc.) based on themagnitude in difference between the deviation value and the deviationthreshold value, based on the number of deviation threshold values thatare exceeded, or combinations thereof (Block 1016).

In this manner, the autonomous driving computer system 144 may identifysensor degradation in a sensor or whether a sensor is operating outsideof normal or preferred operating parameters based on objects in thedriving environment of the autonomous vehicle 102. Determining sensordegradation in the sensors of the autonomous vehicle 102 may includedetermining sensor degradation in different types of sensors. Moreover,the autonomous driving computer system 144 may be configured to performthis determination using moving objects, such as vehicles, andnon-moving objects, such as lane lines, k-rail concrete barriers,billboards, and other such objects. As the sensor degradation detectionmethods and system disclosed herein may be used to determine whetherand/or when a sensor is experiencing a problem, the safety andconfidence of the autonomous vehicle in navigating a driving environmentis enhanced.

Although aspects of this disclosure have been described with referenceto particular embodiments, it is to be understood that these embodimentsare merely illustrative of the principles and applications of thepresent disclosure. It is therefore to be understood that numerousmodifications may be made to the illustrative embodiments and that otherarrangements may be devised without departing from the spirit and scopeof this disclosure as defined by the appended claims. Furthermore, whilecertain operations and functions are shown in a specific order, they maybe performed in a different order unless it is expressly statedotherwise.

The invention claimed is:
 1. A system for determining operating status of a sensor, the system comprising one or more processors, in communication with a sensor of a first vehicle, the one or more processors being configured to: identify at least one movement characteristic of the first vehicle; receive sensor data from the sensor, wherein the sensor data includes data for a second vehicle in a driving environment of the first vehicle; determine a first state information for the second vehicle based on the sensor data; determine a second state information for the second vehicle based on the at least one movement characteristic; compare the first state information with the second state information; and determine the operating status of the sensor based on the comparison.
 2. The system of claim 1, wherein determining the operating status is further based on at least one static object included in the sensor data.
 3. The system of claim 1, wherein the one or more processors are further configured to determine a deviation value based on the comparison, and wherein the deviation value is further used to determine the operating status of the sensor.
 4. The system of claim 3, wherein the one or more processors are further configured to determine if the deviation value meets a threshold value, and wherein the determination is further used to determine the operating status.
 5. The system of claim 4, wherein when the determination includes that the deviation value not meets the threshold value, and the operating status corresponds to the sensor being degraded.
 6. The system of claim 3, wherein the one or more processors are further configured to determine a second deviation value, and wherein the second deviation value is further used to determine the operating status of the sensor.
 7. The system of claim 1, wherein the one or more processors are further configured to, in response to determining the operating status of the sensor, requesting that a driver take control the vehicle.
 8. The system of claim 1, further including a display, and wherein the one or more processors are further configured to, in response to determining the operating status of the sensor, displaying a notification on the display indicating degradation of the sensor.
 9. The system of claim 1, wherein: the second state information comprises an expected parameter; and determining the second state information comprises: determining a change to the at least one movement characteristic of the first vehicle; and determining the expected parameter based on the change to the at least one movement characteristic of the first vehicle.
 10. The system of claim 9, wherein the change to the at least one movement characteristic of the first vehicle corresponds to a change in distance between the first vehicle and the second vehicle.
 11. The system of claim 9, wherein the change to the at least one movement characteristic of the first vehicle corresponds to a change in the speed of the first vehicle.
 12. The system of claim 1, further comprising the first vehicle and the sensor, wherein the one or more processors are located in the first vehicle.
 13. A non-transitory computer-readable medium on which instructions are stored, the instructions, when executed by one or more processors, cause the one or more processors to perform a method for determining operating status of a sensor, the method comprising: identifying at least one movement characteristic of a first vehicle; receiving sensor data from the sensor, wherein the sensor data includes data for a second vehicle in a driving environment of the first vehicle; determining a first state information for the second vehicle based on the sensor data; determining a second state information for the second vehicle based on the at least one movement characteristic; comparing the first state information with the second state information; and determining the operating status of the sensor based on the comparison.
 14. The medium of claim 13, wherein the method further includes determining the operating status is further based on at least one static object included in the sensor data.
 15. The medium of claim 13, wherein the method further includes determining a deviation value based on the comparison, and wherein the deviation value is further used to determine the operating status of the sensor.
 16. The medium of claim 15, wherein the method further includes determining if the deviation value meets a threshold value, and wherein the determination is further used to determine the operating status.
 17. The medium of claim 16, wherein when the determination includes that the deviation value not meets the threshold value, and the operating status corresponds to the sensor being degraded.
 18. The medium of claim 15, wherein the method further comprises determining a second deviation value, and wherein the second deviation value is further used to determine the operating status of the sensor.
 19. The medium of claim 13, wherein the second state information comprises an expected parameter, and determining the second state information comprises: determining a change to the at least one movement characteristic of the first vehicle; and determining the expected parameter based on the change to the at least one movement characteristic of the first vehicle.
 20. The medium of claim 19, wherein the change to the at least one movement characteristic of the first vehicle corresponds to a change in the speed of the first vehicle. 